Vineyard landscape position influences grape nitrogen via soil nitrogen supply: insights from Ion exchange membrane monitoring
Bibliographic record
Abstract
Aims Understanding the impact of landscape on soil nitrogen (N) dynamics is essential for optimizing vineyard productivity and grape quality potential. Our study investigated the effect of site and slope position on soil N supply, grapevine N uptake, and berry quality potential in a Chardonnay vineyard. Methods Combining ion exchange membrane (IEM) technology with plant N tests, we monitored soil mineral N fluxes, nitrogen exposure (NE), and plant responses across two distinct sites (S1: rapid-draining sandy soil; S2: imperfectly drained silt loam) over a growing season. Results We observed distinct site-specific behaviors. S1 exhibited higher mineral N fluxes (up to 4.5 μg N·cm−2·day−1) and NE dominated by nitrate, whereas S2 stored more total N and organic matter. For both sites, lower slope positions showed 33% higher biological N availability, and 19% greater growing season soil N supply compared to upper slopes, linked to higher organic matter. Conclusions Sequential IEM placement effectively tracked plant-available N, expressed as NE, correlating strongly with vine N status. Site and slope position significantly influence N uptake, yield, and berry quality potential of grapevines by altering N mineralization and soil mineral N dynamics. These results provide a tool for optimizing N fertilizer management strategies within landscapes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".